Novel methodology for detection and prediction of mild cognitive impairment using resting‐state EEG

Deng, Jinxian; Sun, Baolin; Kavcic, Voyko; Liu, Mingyan; Giordani, Bruno; Li, Tongtong

doi:10.1002/alz.13411

Cited by 3 publications

(2 citation statements)

References 86 publications

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“…The convergence speeds of CCM and cCCM also vary with the signals under applications and need to be taken into consideration in causality analysis, especially in dynamic systems where the causal relationships are time-variant. It is worthy to point out that when combined with the sliding window approach [47,48], cCCM can be used to evaluate time-varying causality in dynamic networks such as brain networks [49].…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Causalized Convergent Cross Mapping and Its Implementation in Causality Analysis

Sun,

Deng,

Scheel

et al. 2024

Entropy

Self Cite

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Rooted in dynamic systems theory, convergent cross mapping (CCM) has attracted increased attention recently due to its capability in detecting linear and nonlinear causal coupling in both random and deterministic settings. One limitation with CCM is that it uses both past and future values to predict the current value, which is inconsistent with the widely accepted definition of causality, where it is assumed that the future values of one process cannot influence the past of another. To overcome this obstacle, in our previous research, we introduced the concept of causalized convergent cross mapping (cCCM), where future values are no longer used to predict the current value. In this paper, we focus on the implementation of cCCM in causality analysis. More specifically, we demonstrate the effectiveness of cCCM in identifying both linear and nonlinear causal coupling in various settings through a large number of examples, including Gaussian random variables with additive noise, sinusoidal waveforms, autoregressive models, stochastic processes with a dominant spectral component embedded in noise, deterministic chaotic maps, and systems with memory, as well as experimental fMRI data. In particular, we analyze the impact of shadow manifold construction on the performance of cCCM and provide detailed guidelines on how to configure the key parameters of cCCM in different applications. Overall, our analysis indicates that cCCM is a promising and easy-to-implement tool for causality analysis in a wide spectrum of applications.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Causalized Convergent Cross Mapping and Its Implementation in Causality Analysis

Sun,

Deng,

Scheel

et al. 2024

Entropy

Self Cite

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“…Zhuang et al reported spinal cord stimulation may facilitate the recovery of consciousness, and they used an EEG test to predict the process [150]. Thanks to the boom in deep learning, many deep learning models are developed and combined with EEG to detect mental fatigue [151], Parkinson's disease [152], depression [153], schizophrenia [154,155], epilepsy [156][157][158], and neurocognitive disorders [159,160]. In addition to symptom detection, EEG is reported to localize the epileptogenic zone [161,162] and it has the potential to guide the surgery.…”

Section: Bioelectrical Sensorsmentioning

confidence: 99%

Recent Advances in Wearable Healthcare Devices: From Material to Application

Luo,

Tan,

Wen

2024

Bioengineering

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In recent years, the proliferation of wearable healthcare devices has marked a revolutionary shift in the personal health monitoring and management paradigm. These devices, ranging from fitness trackers to advanced biosensors, have not only made healthcare more accessible, but have also transformed the way individuals engage with their health data. By continuously monitoring health signs, from physical-based to biochemical-based such as heart rate and blood glucose levels, wearable technology offers insights into human health, enabling a proactive rather than a reactive approach to healthcare. This shift towards personalized health monitoring empowers individuals with the knowledge and tools to make informed decisions about their lifestyle and medical care, potentially leading to the earlier detection of health issues and more tailored treatment plans. This review presents the fabrication methods of flexible wearable healthcare devices and their applications in medical care. The potential challenges and future prospectives are also discussed.

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A functional neuroimaging biomarker of mild cognitive impairment using TD-fNIRS

Dubois,

Duffy,

Field

et al. 2024

Preprint

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INTRODUCTION: Diagnostic assessments of mild cognitive impairment (MCI) are lengthy and burdensome, highlighting the need for new tools to detect MCI. Time-domain functional near-infrared spectroscopy (TD-fNIRS) can measure brain function in clinical settings and may address this need. METHODS: MCI patients (n=50) and age-matched healthy controls (HC; n=51) underwent TD-fNIRS recordings during cognitive tasks (verbal fluency, N-back). Machine learning models were trained to distinguish MCI from HC using neural activity, cognitive task behavior, and self-reported impairment as input features. RESULTS: Significant group-level differences (MCI vs HC) were demonstrated in self-report, N-back and verbal fluency behavior, and task-related brain activation. Classifier performance was similar when using self-report (AUC=0.76) and self-report plus behavior (AUC=0.79) as input features, but was strongest when neural metrics were included (AUC=0.92). DISCUSSION: This study demonstrates the potential of TD-fNIRS to assess MCI with short brain scans in clinical settings.

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Novel methodology for detection and prediction of mild cognitive impairment using resting‐state EEG

Cited by 3 publications

References 86 publications

Causalized Convergent Cross Mapping and Its Implementation in Causality Analysis

Causalized Convergent Cross Mapping and Its Implementation in Causality Analysis

Recent Advances in Wearable Healthcare Devices: From Material to Application

A functional neuroimaging biomarker of mild cognitive impairment using TD-fNIRS

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